We retrieved the pre-processed data of several publicly available studies from GEO (see main text for details). In this Section, we plot the PCA of each dataset using the original normalization.

Starting from the data as normalized by the authors, or applying UQ scaling normalization if the authors provided only raw counts, we apply RUVs using all the genes as negative controls and choosing the value of k that led to the best looking RLE plot. For each dataset, we retained only the genes expressed in at least three replicate samples.

This analysis is intended to show that published normalized datasets often show residual unwanted variation and that RUVs can remove unwanted variation when present and does not compromise the data when scaling normalization is working well. A more careful analysis of each dataset, e.g. by selecting a problem-specific set of negative control genes, could lead to better results.

GSE60261

dicer KO mRNA data, DESeq2 normalization

data <- read.table("GEO_datasets/GSE60261.txt", header = TRUE, sep = "\t")
dicerKOmRNA <- as.matrix(data[, 2:ncol(data)])
x <- as.factor(rep(c("KO", "WT"), each = 3))
colLib <- colors[x]

boxplot(log(dicerKOmRNA + 1), las = 2)

filter <- apply(dicerKOmRNA, 1, function(x) length(x[which(x > 0)]) > 3)
table(filter)
## filter
## FALSE  TRUE 
##  8555 68383
filtered <- dicerKOmRNA[filter, ]

plotRLE(filtered, col = colLib, outline = FALSE, ylim = c(-0.4, 0.4), las = 2)

plotPCA(filtered, col = colLib)

groups <- matrix(data = c(1:3, 4:6), nrow = 2, byrow = TRUE)
s <- RUVs(round(filtered), 1:nrow(filtered), k = 1, groups)

plotRLE(s$normalizedCounts, col = colLib, outline = FALSE, ylim = c(-0.4, 0.4), las = 2)

plotPCA(s$normalizedCounts, col = colLib)

GSE60262

dicer KO small RNA data, DESeq2 normalization

data <- read.table("GEO_datasets/GSE60262.txt", header = TRUE, sep = "\t")
dicerKOsmallRNA <- as.matrix(data[, 2:ncol(data)])
x <- as.factor(rep(c("KO", "WT"), each = 3))
colLib <- colors[x]

boxplot(log(dicerKOsmallRNA + 1), las = 2)

filter <- apply(dicerKOsmallRNA, 1, function(x) length(x[which(x > 0)]) > 3)
table(filter)
## filter
## FALSE  TRUE 
## 69701 39407
filtered <- dicerKOsmallRNA[filter, ]

plotRLE(filtered, col = colLib, outline = FALSE, ylim = c(-1, 1), las = 2)

plotPCA(filtered, col = colLib)

groups <- matrix(data = c(1:3, 4:6), nrow = 2, byrow = TRUE)
s <- RUVs(round(filtered), 1:nrow(filtered), k = 2, groups, round = FALSE)

plotRLE(s$normalizedCounts, col = colLib, outline = FALSE, ylim = c(-1, 1), las = 2)

plotPCA(s$normalizedCounts, col = colLib)

GSE58797

H2A.Z shRNA knckdown FPKM, and FPKM+UQ

data <- read.table("GEO_datasets/GSE58797.txt", header = TRUE, sep = "\t")
H2AZRNA <- as.matrix(data[, 2:10])
x <- as.factor(rep(c("Control", "shRNA", "FC"), each = 3))
colLib <- colors[x]

boxplot(log(H2AZRNA + 1), las = 2)

filter <- apply(H2AZRNA, 1, function(x) length(x[which(x > 0)]) > 3)
table(filter)
## filter
## FALSE  TRUE 
##  7518 16971
filtered <- H2AZRNA[filter, ]

H2AZFPKMuq <- betweenLaneNormalization(filtered, which = "upper", round = FALSE)

boxplot(log(H2AZFPKMuq + 1), las = 2)

plotRLE(H2AZFPKMuq, col = colLib, outline = FALSE, ylim = c(-0.2, 0.2), las = 2)

plotPCA(H2AZFPKMuq, col = colLib)

groups <- matrix(data = c(1:3, 4:6, 7:9), nrow = 3, byrow = TRUE)
s <- RUVs(round(H2AZFPKMuq), 1:nrow(H2AZFPKMuq), k = 1, groups, round = FALSE)

plotRLE(s$normalizedCounts, col = colLib, outline = FALSE, ylim = c(-0.2, 0.2), las = 2)

plotPCA(s$normalizedCounts, col = colLib)

GSE61915

aging 3M-24 M SE filter >10 reads, DESeq

data <- read.table("GEO_datasets/GSE61915.txt", header = TRUE, sep = "\t")
agingRNASE <- as.matrix(data[, 2:ncol(data)])
x <- as.factor(c(rep("Young", 5), rep("Old", 6)))
colLib <- colors[x]

boxplot(log(agingRNASE + 1), las = 2)

filter <- apply(agingRNASE, 1, function(x) length(x[which(x > 0)]) > 3)
table(filter)
## filter
##  TRUE 
## 15436
filtered <- agingRNASE[filter, ]

plotRLE(filtered, col = colLib, outline = FALSE, ylim = c(-0.5, 0.5), las = 2)

plotPCA(filtered, col = colLib)

groups <- matrix(data = c(1:5, -1, 6:11), nrow = 2, byrow = TRUE)
s <- RUVs(round(filtered), 1:nrow(filtered), k = 1, groups)

plotRLE(s$normalizedCounts, col = colLib, outline = FALSE, ylim = c(-0.5, 0.5), las = 2)

plotPCA(s$normalizedCounts, col = colLib)

GSE53380

Kat2a KO and NOR, UQ

data <- read.table("GEO_datasets/GSE53380.txt", header = TRUE, sep = "\t")
KO_NORRNA <- as.matrix(data[, 2:ncol(data)])
x <- as.factor(c(rep("Control", 6), rep("NOR", 5), rep("KO", 6), rep("KO_NOR", 7)))
colLib <- colors[x]

boxplot(log(KO_NORRNA + 1), las = 2)

filter <- apply(KO_NORRNA, 1, function(x) length(x[which(x > 0)]) > 3)
table(filter)
## filter
## FALSE  TRUE 
## 14203 24152
filtered <- KO_NORRNA[filter, ]

uq <- betweenLaneNormalization(filtered, which = "upper")
boxplot(log(uq + 1), las = 2)

plotRLE(uq, col = colLib, outline = FALSE, ylim = c(-0.6, 0.6), las = 2)

plotPCA(uq, col = colLib)

groups <- matrix(data = c(1:6, -1, 7:11, -1, -1, 12:17, -1, 18:24), nrow = 4, byrow = TRUE)
s <- RUVs(uq, 1:nrow(uq), k = 2, groups)

plotRLE(s$normalizedCounts, col = colLib, outline = FALSE, ylim = c(-0.6, 0.6), las = 2)

plotPCA(s$normalizedCounts, col = colLib)

GSE65159

AD mouse model, 2 time points

data <- read.table("GEO_datasets/GSE65159.txt", header = TRUE, sep = "\t")
AD_RNA <- as.matrix(data[, 2:ncol(data)])
x <- as.factor(rep(c("Control6wk", "AD6wk", "Control2wk", "AD2wk"), each = 3))
colLib <- colors[x]

boxplot(log(AD_RNA + 1), las = 2)

filter <- apply(AD_RNA, 1, function(x) length(x[which(x > 0)]) > 3)
table(filter)
## filter
## FALSE  TRUE 
## 18477 19514
filtered <- AD_RNA[filter, ]

uq <- betweenLaneNormalization(filtered, which = "upper")
boxplot(log(uq + 1), las = 2)

plotRLE(uq, col = colLib, outline = FALSE, ylim = c(-1, 1), las = 2)

plotPCA(uq, col = colLib)

groups <- matrix(data = c(1:3, 4:6, 7:9, 10:12), nrow = 4, byrow = TRUE)
s <- RUVs(uq, 1:nrow(uq), k = 1, groups)

plotRLE(s$normalizedCounts, col = colLib, outline = FALSE, ylim = c(-1, 1), las = 2)

plotPCA(s$normalizedCounts, col = colLib)

GSE58343

ribosome bound, FPKM PE and SE

data <- read.table("GEO_datasets/GSE58343.txt", header = TRUE, sep = "\t")
Ribosome_RNA <- as.matrix(data[, 2:ncol(data)])
x <- as.factor(c(rep("HC", 10), rep("FC", 12)))
colLib <- colors[x]

boxplot(log(Ribosome_RNA + 1), las = 2)

filter <- apply(Ribosome_RNA, 1, function(x) length(x[which(x > 0)]) > 3)
table(filter)
## filter
## FALSE  TRUE 
## 16117 13731
filtered <- Ribosome_RNA[filter, ]

boxplot(log(filtered + 1), las = 2)

plotRLE(filtered, col = colLib, outline = FALSE, ylim = c(-1, 2), las = 2)

plotPCA(filtered, col = colLib)

groups <- matrix(data = c(1, 3, 6, 8, 4, 9, -1, -1, 2, 5, 7, 10, 11, 13, 17, 19, 
    14, 20, -1, -1, 16, 22, -1, -1, 12, 15, 18, 21), nrow = 7, byrow = TRUE)
s <- RUVs(round(filtered), 1:nrow(filtered), k = 1, groups, round = FALSE)

plotRLE(s$normalizedCounts, col = colLib, outline = FALSE, ylim = c(-1, 2), las = 2)

plotPCA(s$normalizedCounts, col = colLib)

Session Info

sessionInfo()
## R version 3.2.0 (2015-04-16)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.3 (Yosemite)
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils    
## [7] datasets  methods   base     
## 
## other attached packages:
##  [1] RColorBrewer_1.1-2      RUVSeq_1.2.0           
##  [3] edgeR_3.10.2            limma_3.24.5           
##  [5] EDASeq_2.2.0            ShortRead_1.26.0       
##  [7] GenomicAlignments_1.4.1 Rsamtools_1.20.4       
##  [9] GenomicRanges_1.20.4    GenomeInfoDb_1.4.0     
## [11] Biostrings_2.36.1       XVector_0.8.0          
## [13] IRanges_2.2.2           S4Vectors_0.6.0        
## [15] BiocParallel_1.2.2      Biobase_2.28.0         
## [17] BiocGenerics_0.14.0     BiocStyle_1.6.0        
## 
## loaded via a namespace (and not attached):
##  [1] formatR_1.2          futile.logger_1.4.1 
##  [3] R.methodsS3_1.7.0    futile.options_1.0.0
##  [5] bitops_1.0-6         R.utils_2.1.0       
##  [7] tools_3.2.0          zlibbioc_1.14.0     
##  [9] digest_0.6.8         RSQLite_1.0.0       
## [11] annotate_1.46.0      evaluate_0.7        
## [13] lattice_0.20-31      DBI_0.3.1           
## [15] yaml_2.1.13          DESeq_1.20.0        
## [17] genefilter_1.50.0    stringr_1.0.0       
## [19] hwriter_1.3.2        knitr_1.10.5        
## [21] aroma.light_2.4.0    grid_3.2.0          
## [23] AnnotationDbi_1.30.1 survival_2.38-1     
## [25] XML_3.98-1.2         rmarkdown_0.6.1     
## [27] latticeExtra_0.6-26  geneplotter_1.46.0  
## [29] lambda.r_1.1.7       magrittr_1.5        
## [31] MASS_7.3-40          splines_3.2.0       
## [33] htmltools_0.2.6      matrixStats_0.14.0  
## [35] xtable_1.7-4         stringi_0.4-1       
## [37] R.oo_1.19.0